CN111538743A - SQL-based data blood relationship analysis method and system - Google Patents
SQL-based data blood relationship analysis method and system Download PDFInfo
- Publication number
- CN111538743A CN111538743A CN202010319878.7A CN202010319878A CN111538743A CN 111538743 A CN111538743 A CN 111538743A CN 202010319878 A CN202010319878 A CN 202010319878A CN 111538743 A CN111538743 A CN 111538743A
- Authority
- CN
- China
- Prior art keywords
- data
- sql
- blood relationship
- analysis
- abstract syntax
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a data blood relationship analysis method and a system based on SQL, wherein the method comprises the steps of firstly extracting a regularized SQL statement from a script file containing an SQL code, and finishing the cleaning of the SQL statement; performing lexical analysis on the regular SQL sentences to generate abstract syntax trees, and traversing the abstract syntax trees to perform syntactic analysis on the SQL sentences; then, blood relationship analysis is carried out on the SQL statement according to the abstract syntax tree to obtain a blood relationship analysis result; and finally, drawing a data blood relationship graph of the SQL statement according to the blood relationship analysis result, and carrying out visual display. The invention displays the blood relationship among the tables of the database in a graphical mode, can card out the dependency relationship among the tables and the fields, and is convenient for the inquiry, development and management of subsequent services.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a data blood relationship analysis method and system based on SQL.
Background
With the development and application of big data technology, the requirement of data blood relationship analysis appears in the field of big data governance, and the mapping relation of tables and fields between databases can be combed through the analysis and processing of structured query statements operated on the databases to form a data map, so that the flow direction of data can be tracked in massive and huge data, the source of upstream data in a big data system and the destination of downstream data can be checked, big data developers and business analysts can conveniently master the consumption and source of the data, the big data system can be better managed, and the value of the data blood relationship can be mined. However, the existing database blood relationship analysis method is usually based on the database blood relationship storage, which is not beneficial to installation, configuration and maintenance of various departments of big data, and this results in low efficiency of data blood relationship analysis.
Disclosure of Invention
The invention aims to solve the problem that the existing database blood relationship analysis method is low in analysis efficiency, and provides a data blood relationship analysis method and system based on SQL.
The technical scheme of the invention is as follows: the SQL-based data blood relationship analysis method comprises the following steps:
and S1, extracting the regularized SQL statement from the script file containing the SQL code, and finishing the cleaning of the SQL statement.
And S2, performing lexical analysis on the regular SQL sentences to generate abstract syntax trees, and traversing the abstract syntax trees to perform syntactic analysis on the SQL sentences.
And S3, performing blood relationship analysis on the SQL statement according to the abstract syntax tree to obtain a blood relationship analysis result.
And S4, drawing a data blood relationship graph of the SQL statement according to the blood relationship analysis result, and performing visual display.
Further, step S1 includes the following substeps:
s11, acquiring the script file containing the SQL code and searching the flag bit of the SQL code.
And S12, filtering irrelevant contents in the script file by using the flag bit, and reserving to obtain a regularized SQL code statement.
Further, step S2 includes the following substeps:
s21, performing lexical analysis on the regulated SQL sentences, performing keyword division on the regulated SQL sentences according to grammar rules, and performing label identification on each keyword.
And S22, generating an abstract syntax tree by taking each identified SQL statement as a node.
And S23, traversing the abstract syntax tree, giving a syntactic meaning to the SQL statement corresponding to each label, and realizing the syntactic analysis of the SQL statement.
Further, step S3 includes the following substeps:
and S31, processing the node data identified in the abstract syntax tree, taking the source data table and the source data field as input sets of the node data, and taking the target data table and the target data field as output sets of the node data.
And S32, respectively mapping the source and the destination of the node data to obtain a blood relationship analysis result.
Further, step S4 includes the following substeps:
and S41, drawing data tables and field nodes in the data consanguinity relationship graph according to the node data in the input set and the output set.
And S42, associating nodes in the data blood relationship graph and drawing arrow pointing connecting lines according to the blood relationship analysis result to finish drawing the data blood relationship graph of the SQL statement.
And S43, sending the drawn data blood relationship graph to a user terminal for visual display.
The invention also provides a SQL-based data blood relationship analysis system, which comprises a data cleaning module, a data analysis module, a blood relationship analysis module and a visualization module which are connected in sequence; the data cleaning module is used for extracting a regularized SQL statement from a script file containing an SQL code and finishing cleaning the SQL statement; the data analysis module is used for performing lexical analysis on the regular SQL sentences to generate an abstract syntax tree and traversing the abstract syntax tree to perform syntactic analysis on the SQL sentences; the blood relationship analysis module is used for carrying out blood relationship analysis on the SQL statement according to the abstract syntax tree to obtain a blood relationship analysis result; and the visualization module is used for drawing a data blood relationship graph of the SQL statement according to the blood relationship analysis result and carrying out visualization display.
Further, the data cleaning module comprises a marking unit and a filtering unit which are connected with each other; the marking unit is used for acquiring a script file containing an SQL code and searching a flag bit of the SQL code; the filtering unit is used for filtering irrelevant contents in the script file by using the flag bit and reserving and obtaining a regularized SQL code statement.
Furthermore, the data analysis module comprises a lexical analysis unit and a syntactic analysis unit which are connected with each other; the lexical analysis unit is used for performing lexical analysis on the regularized SQL sentences, performing keyword division on the regularized SQL sentences according to grammar rules, performing label identification on each keyword, and generating an abstract syntax tree by taking each identified SQL sentence as a node; and the syntax parsing unit is used for traversing the abstract syntax tree, giving syntax meaning to the SQL statement corresponding to each label and realizing syntax parsing of the SQL statement.
Further, the blood margin analysis module comprises a node identification unit and a blood margin association unit which are connected in sequence; the node identification unit is used for processing node data identified in the abstract syntax tree, taking a source data table and a source data field as input sets of the node data, and taking a target data table and a target data field as output sets of the node data; the blood relationship correlation unit is used for mapping the source and the destination of the node data respectively to obtain a blood relationship analysis result.
Further, the visualization module comprises a front end drawing unit and a rear end sending unit which are connected with each other; the front end drawing unit is used for drawing data tables and field nodes in the data blood relationship graph, associating the nodes in the data blood relationship graph and drawing arrow pointing connecting lines according to blood relationship analysis results; and the rear end sending unit is used for sending the drawn data blood relationship graph to a user terminal for visual display.
The invention has the beneficial effects that:
(1) the invention can avoid analyzing the blood relationship of SQL sentences by using a database, and can display the dependency relationship between data in a visual mode, thereby facilitating the inquiry, development and management of subsequent services.
(2) The invention strengthens the monitoring of data flow through the data blood relationship analysis process and clears the source and the destination of the data in a visual mode.
Drawings
Fig. 1 is a flowchart of a method for analyzing data blood relationship based on SQL according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating a visualization effect of a user terminal according to an embodiment of the present invention.
Fig. 3 is a block diagram of an SQL-based data relationship analysis system according to a second embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It is to be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, not to limit the scope of the invention.
The first embodiment is as follows:
the embodiment of the invention provides a data blood relationship analysis method based on SQL, which comprises the following steps S1-S4 as shown in FIG. 1:
and S1, extracting the regularized SQL statement from the script file containing the SQL code, and finishing the cleaning of the SQL statement.
The step S1 includes the following substeps S11-S12:
s11, acquiring the script file containing the SQL code and searching the flag bit of the SQL code.
In the embodiment of the invention, the script file is a script such as perl.
And S12, filtering irrelevant contents in the script file by using the flag bit, and reserving to obtain a regularized SQL code statement.
According to the embodiment of the invention, the SQL statement cleaning example is as follows:
CREATE TABLE
${icl_db_name}.C_PT_CUSTODY_BAL_ACCUM_TMP_ACCT${TX_DATE}
(Party_Id VARCHAR(120),Acct_Bal DECIMAL(18,2))
CLUSTERED BY
(Party_Id)
INTO 3BUCKETS
STORED AS ORC.
where the sign flag of the $ { } variable parameter with explicit characteristics needs to be filtered. For example, here icl _ db _ name is the name of the database, followed by the table name and date of the database. After the SQL statement cleaning and filtering is performed, the following results can be obtained:
CREATE TABLE icl_db_name.C_PT_CUSTODY_BAL_ACCUM_TMP_ACCTTX_DATE(Party_Id VARCHAR(120),Acct_Bal DECIMAL(18,2))BY(Party_Id)INTO 3BUCKETSSTORED AS ORC.
and S2, performing lexical analysis on the regular SQL sentences to generate abstract syntax trees, and traversing the abstract syntax trees to perform syntactic analysis on the SQL sentences.
The step S2 includes the following substeps S21-S23:
s21, performing lexical analysis on the regulated SQL sentences, performing keyword division on the regulated SQL sentences according to grammar rules, and performing label identification on each keyword.
And S22, generating an abstract syntax tree by taking each identified SQL statement as a node.
And S23, traversing the abstract syntax tree, giving a syntactic meaning to the SQL statement corresponding to each label, and realizing the syntactic analysis of the SQL statement.
According to the embodiment of the invention, the SQL statement parsing example is as follows:
<DDL'CREATE'at 0x7FDCCC579828>,
<Whitespace”at 0x7FDCCC579888>,
<Keyword'TABLE'at 0x7FDCCC5799A8>,
<Whitespace”at 0x7FDCCC579948>,
<Identifier'icl_db...'at 0x7FDCCC642E58>,
<Whitespace”at 0x7FDCCC579AC8>,
<Parenthesis'(Party...'at 0x7FDCCC642C78>,
<Whitespace”at 0x7FDCCC56B228>,
<Keyword'BY'at 0x7FDCCC56B288>,
<Whitespace”at 0x7FDCCC56B2E8>,
<Parenthesis'(Party...'at 0x7FDCCC642CF0>,
<Whitespace”at 0x7FDCCC56B468>,
<Keyword'INTO'at 0x7FDCCC56B4C8>,
<Whitespace”at 0x7FDCCC56B528>,
<Identifier'3BUCK...'at 0x7FDCCC572318>,
<Whitespace”at 0x7FDCCC56B6A8>,
<Identifier'STORED...'at 0x7FDCCC572228>
the lexical analysis divides a SQL statement INTO different keywords, such as 'CREATE', 'TABLE', 'BY', 'INTO', and the like. These keywords are assigned different identifiers that mark the lexical meaning of the keywords in the SQL statement, such as DDL, Keyword, Parenthesis, Identifier, etc.
And S3, performing blood relationship analysis on the SQL statement according to the abstract syntax tree to obtain a blood relationship analysis result.
The step S3 includes the following substeps S31-S32:
and S31, processing the node data identified in the abstract syntax tree, taking the source data table and the source data field as input sets of the node data, and taking the target data table and the target data field as output sets of the node data.
And S32, respectively mapping the source and the destination of the node data to obtain a blood relationship analysis result.
In an embodiment of the present invention, the keywords identifying the node data source tags include 'FROM', 'JOIN', 'connect', 'LEFT JOIN', 'RIGHT JOIN', 'LEFT out JOIN', 'RIGHT out JOIN', 'full JOIN', 'CROSS JOIN', and the like, and the keywords identifying the node data source tags and the node data destination tags include 'intra', 'over write', 'TABLE', and sets of data sources and data destinations are respectively identified by the keywords. And performing blood relationship analysis on different SQL operations including CREATE, INSERT, SELECT and the like, and outputting the source and destination mapping of the node data as blood relationship analysis results.
And S4, drawing a data blood relationship graph of the SQL statement according to the blood relationship analysis result, and performing visual display.
The step S4 includes the following substeps S41-S43:
and S41, drawing data tables and field nodes in the data consanguinity relationship graph according to the node data in the input set and the output set.
And S42, associating nodes in the data blood relationship graph and drawing arrow pointing connecting lines according to the blood relationship analysis result to finish drawing the data blood relationship graph of the SQL statement.
And S43, sending the drawn data blood relationship graph to a user terminal for visual display.
In the embodiment of the invention, the blood relationship processing function takes a flash frame as a back-end service, when a user terminal needs to visualize blood relationship, the back-end service returns corresponding blood relationship nodes and association results to the user terminal, a data blood relationship graph is drawn through a pinker.js frame, and the effect displayed at the user terminal is shown in figure 2.
Example two:
the embodiment of the invention provides a data blood relationship analysis system based on SQL, which comprises a data cleaning module, a data analysis module, a blood relationship analysis module and a visualization module which are connected in sequence as shown in figure 3.
The data cleaning module is used for extracting a regularized SQL statement from a script file containing an SQL code and completing cleaning of the SQL statement; the data analysis module is used for performing lexical analysis on the regular SQL sentences to generate an abstract syntax tree and traversing the abstract syntax tree to perform syntactic analysis on the SQL sentences; the blood relationship analysis module is used for carrying out blood relationship analysis on the SQL statement according to the abstract syntax tree to obtain a blood relationship analysis result; and the visualization module is used for drawing a data blood relationship graph of the SQL statement according to the blood relationship analysis result and carrying out visualization display.
As shown in fig. 3, the data cleansing module includes a marking unit and a filtering unit connected to each other.
The marking unit is used for acquiring a script file containing an SQL code and searching a flag bit of the SQL code; the filtering unit is used for filtering irrelevant contents in the script file by using the flag bit and reserving and obtaining a regularized SQL code statement.
As shown in fig. 3, the data parsing module includes a lexical parsing unit and a syntactic parsing unit connected to each other.
The lexical analysis unit is used for performing lexical analysis on the regularized SQL sentences, performing keyword division on the regularized SQL sentences according to grammar rules, performing label identification on each keyword, and generating an abstract syntax tree by taking each identified SQL sentence as a node; and the syntax parsing unit is used for traversing the abstract syntax tree, giving syntax meaning to the SQL statement corresponding to each label and realizing syntax parsing of the SQL statement.
As shown in fig. 3, the blood margin analysis module includes a node identification unit and a blood margin association unit, which are connected in sequence.
The node identification unit is used for processing node data identified in the abstract syntax tree, taking a source data table and a source data field as input sets of the node data, and taking a target data table and a target data field as output sets of the node data; the blood relationship correlation unit is used for mapping the source and the destination of the node data respectively to obtain a blood relationship analysis result.
As shown in fig. 3, the visualization module includes a front end rendering unit and a back end transmitting unit connected to each other.
The front end drawing unit is used for drawing data tables and field nodes in the data blood relationship graph, associating the nodes in the data blood relationship graph and drawing arrow pointing connecting lines according to blood relationship analysis results; and the rear end sending unit is used for sending the drawn data blood relationship graph to a user terminal for visual display.
In the embodiment of the invention, the data blood relationship analysis system is developed based on flash, sqlparse, sqllineage, pinker. js and the like, so that the data blood relationship analysis system based on SQL sentences is realized, and the visual display of blood relationship analysis results is completed.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (10)
1. The SQL-based data blood relationship analysis method is characterized by comprising the following steps of:
s1, extracting a regularized SQL statement from the script file containing the SQL code, and finishing the cleaning of the SQL statement;
s2, performing lexical analysis on the regularized SQL sentences to generate abstract syntax trees, and traversing the abstract syntax trees to perform syntax analysis on the SQL sentences;
s3, performing blood relationship analysis on the SQL statement according to the abstract syntax tree to obtain a blood relationship analysis result;
and S4, drawing a data blood relationship graph of the SQL statement according to the blood relationship analysis result, and performing visual display.
2. The method for analyzing data consanguinity relationship according to claim 1, wherein said step S1 includes the following substeps:
s11, acquiring a script file containing the SQL code and searching a flag bit of the SQL code;
and S12, filtering irrelevant contents in the script file by using the flag bit, and reserving to obtain a regularized SQL code statement.
3. The method for analyzing data consanguinity relationship according to claim 1, wherein said step S2 includes the following substeps:
s21, performing lexical analysis on the regularized SQL sentences, performing keyword division on the regularized SQL sentences according to grammar rules, and performing label identification on each keyword;
s22, taking each marked SQL statement as a node to generate an abstract syntax tree;
and S23, traversing the abstract syntax tree, giving a syntactic meaning to the SQL statement corresponding to each label, and realizing the syntactic analysis of the SQL statement.
4. The method for analyzing data consanguinity relationship according to claim 3, wherein said step S3 includes the following substeps:
s31, processing the node data identified in the abstract syntax tree, taking the source data table and the source data field as the input set of the node data, and taking the target data table and the target data field as the output set of the node data;
and S32, respectively mapping the source and the destination of the node data to obtain a blood relationship analysis result.
5. The method for analyzing data consanguinity relationship according to claim 4, wherein said step S4 includes the following substeps:
s41, drawing a data table and field nodes in the data blood relationship graph according to the node data in the input set and the output set;
s42, associating nodes in the data blood relationship graph and drawing arrow pointing connecting lines according to the blood relationship analysis result to finish drawing the data blood relationship graph of the SQL statement;
and S43, sending the drawn data blood relationship graph to a user terminal for visual display.
6. The SQL-based data blood relationship analysis system is characterized by comprising a data cleaning module, a data analysis module, a blood relationship analysis module and a visualization module which are sequentially connected;
the data cleaning module is used for extracting a regularized SQL statement from a script file containing an SQL code and completing cleaning of the SQL statement;
the data analysis module is used for performing lexical analysis on the regular SQL sentences to generate an abstract syntax tree and traversing the abstract syntax tree to perform syntactic analysis on the SQL sentences;
the blood relationship analysis module is used for carrying out blood relationship analysis on the SQL statement according to the abstract syntax tree to obtain a blood relationship analysis result;
and the visualization module is used for drawing a data blood relationship graph of the SQL statement according to the blood relationship analysis result and carrying out visualization display.
7. The data relationship analysis system of claim 6, wherein the data washing module comprises a labeling unit and a filtering unit connected to each other;
the marking unit is used for acquiring a script file containing an SQL code and searching a flag bit of the SQL code;
and the filtering unit is used for filtering irrelevant contents in the script file by using the flag bit and reserving and obtaining a regularized SQL code statement.
8. The system according to claim 6, wherein the data parsing module comprises a lexical parsing unit and a syntactic parsing unit connected to each other;
the lexical analysis unit is used for performing lexical analysis on the regularized SQL sentences, performing keyword division on the regularized SQL sentences according to grammar rules, performing label identification on each keyword, and generating an abstract syntax tree by taking each identified SQL sentence as a node;
the syntax parsing unit is used for traversing the abstract syntax tree, giving syntax meaning to the SQL sentences corresponding to the labels and realizing syntax parsing of the SQL sentences.
9. The data relationship analysis system of claim 8, wherein the blood relationship analysis module comprises a node identification unit and a blood relationship association unit connected in sequence;
the node identification unit is used for processing node data identified in the abstract syntax tree, taking a source data table and a source data field as input sets of the node data, and taking a target data table and a target data field as output sets of the node data;
and the blood margin association unit is used for mapping the source and the destination of the node data respectively to obtain a blood margin relation analysis result.
10. The system according to claim 9, wherein the visualization module comprises a front end rendering unit and a back end sending unit connected to each other;
the front end drawing unit is used for drawing data tables and field nodes in the data blood relationship graph, associating the nodes in the data blood relationship graph and drawing arrow pointing connecting lines according to blood relationship analysis results;
and the rear-end sending unit is used for sending the drawn data blood relationship graph to a user terminal for visual display.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010319878.7A CN111538743B (en) | 2020-04-22 | 2020-04-22 | SQL-based data blood relationship analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010319878.7A CN111538743B (en) | 2020-04-22 | 2020-04-22 | SQL-based data blood relationship analysis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111538743A true CN111538743A (en) | 2020-08-14 |
| CN111538743B CN111538743B (en) | 2023-08-18 |
Family
ID=71979108
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010319878.7A Active CN111538743B (en) | 2020-04-22 | 2020-04-22 | SQL-based data blood relationship analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111538743B (en) |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112256721A (en) * | 2020-10-21 | 2021-01-22 | 平安科技（深圳）有限公司 | SQL statement parsing method, system, computer device and storage medium |
| CN112347123A (en) * | 2020-11-10 | 2021-02-09 | 北京金山云网络技术有限公司 | Data blood margin analysis method and device and server |
| CN112363713A (en) * | 2020-11-30 | 2021-02-12 | 杭州玳数科技有限公司 | Binding type SQL blood margin analysis data flow visualization interaction method |
| CN112434046A (en) * | 2020-12-16 | 2021-03-02 | 杭州天均科技有限公司 | Data blood margin analysis method, device, equipment and storage medium |
| CN112464050A (en) * | 2020-12-11 | 2021-03-09 | 中国人寿保险股份有限公司 | Data blood margin sorting method and device based on python and electronic equipment |
| CN112749186A (en) * | 2021-01-22 | 2021-05-04 | 广州虎牙科技有限公司 | Data processing method and device, electronic equipment and computer readable storage medium |
| CN112765209A (en) * | 2021-02-07 | 2021-05-07 | 浪潮云信息技术股份公司 | SQL statement syntax migration method and system between databases |
| CN112818015A (en) * | 2021-01-21 | 2021-05-18 | 广州汇通国信科技有限公司 | Data tracking method, system and storage medium based on data blood margin analysis |
| CN112860585A (en) * | 2021-03-31 | 2021-05-28 | 中国工商银行股份有限公司 | Test script assertion generation method and device |
| CN112925777A (en) * | 2021-02-25 | 2021-06-08 | 吉林亿联银行股份有限公司 | Method and system for detecting data blood margin of HIVE database |
| CN113177057A (en) * | 2021-04-28 | 2021-07-27 | 深圳依时货拉拉科技有限公司 | SQL statement syntax visualization analysis method, system and computer readable storage medium |
| CN113326401A (en) * | 2021-06-16 | 2021-08-31 | 上海哔哩哔哩科技有限公司 | Method and system for generating field blood margin |
| CN113672628A (en) * | 2021-10-22 | 2021-11-19 | 中航金网(北京)电子商务有限公司 | Data blood margin analysis method, terminal device and medium |
| CN113742368A (en) * | 2021-09-16 | 2021-12-03 | 北京航空航天大学 | Data blood relationship analysis method |
| CN114265945A (en) * | 2021-12-30 | 2022-04-01 | 多点生活(武汉)科技有限公司 | Blood relationship extraction method and device and electronic equipment |
| CN114329082A (en) * | 2021-11-19 | 2022-04-12 | 海纳致远数字科技(上海)有限公司 | Data blood relationship analysis method and system based on hugagraph |
| WO2022188331A1 (en) * | 2021-03-11 | 2022-09-15 | 北京锐安科技有限公司 | Method and apparatus for presenting data kinship, and electronic device and storage medium |
| CN115291889A (en) * | 2022-09-27 | 2022-11-04 | 华控清交信息科技(北京)有限公司 | Data blood relationship establishing method and device and electronic equipment |
| CN118035204A (en) * | 2024-01-25 | 2024-05-14 | 深圳市前海研祥亚太电子装备技术有限公司 | Data blood edge display method, distributed task scheduling system and storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104636478A (en) * | 2015-02-13 | 2015-05-20 | 广州神马移动信息科技有限公司 | Information query method and device |
| CN105912595A (en) * | 2016-04-01 | 2016-08-31 | 华南理工大学 | Data origin collection method of relational databases |
| KR101737578B1 (en) * | 2015-11-27 | 2017-05-18 | 한국비앤에스시스템 주식회사 | Method and device for automatically tuning for sql sentences generated automatically |
| CN107644073A (en) * | 2017-09-18 | 2018-01-30 | 广东中标数据科技股份有限公司 | A kind of field consanguinity analysis method, system and device based on depth-first traversal |
| CN107797916A (en) * | 2016-11-14 | 2018-03-13 | 平安科技（深圳）有限公司 | DDL sentences checking method and device |
| CN109325078A (en) * | 2018-09-18 | 2019-02-12 | 拉扎斯网络科技（上海）有限公司 | Data blood margin determination method and device based on structural data |
| CN109446279A (en) * | 2018-10-15 | 2019-03-08 | 顺丰科技有限公司 | Based on neo4j big data genetic connection management method, system, equipment and storage medium |
| CN110232056A (en) * | 2019-05-21 | 2019-09-13 | 苏宁云计算有限公司 | A kind of the blood relationship analytic method and its tool of structured query language |
| CN110245270A (en) * | 2019-05-09 | 2019-09-17 | 重庆天蓬网络有限公司 | Data genetic connection storage method, system, medium and equipment based on graph model |
| CN110555032A (en) * | 2019-09-09 | 2019-12-10 | 北京搜狐新媒体信息技术有限公司 | Data blood relationship analysis method and system based on metadata |
| CN110908997A (en) * | 2019-10-09 | 2020-03-24 | 支付宝(杭州)信息技术有限公司 | Data blood margin construction method and device, server and readable storage medium |
-
2020
- 2020-04-22 CN CN202010319878.7A patent/CN111538743B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104636478A (en) * | 2015-02-13 | 2015-05-20 | 广州神马移动信息科技有限公司 | Information query method and device |
| KR101737578B1 (en) * | 2015-11-27 | 2017-05-18 | 한국비앤에스시스템 주식회사 | Method and device for automatically tuning for sql sentences generated automatically |
| CN105912595A (en) * | 2016-04-01 | 2016-08-31 | 华南理工大学 | Data origin collection method of relational databases |
| CN107797916A (en) * | 2016-11-14 | 2018-03-13 | 平安科技（深圳）有限公司 | DDL sentences checking method and device |
| CN107644073A (en) * | 2017-09-18 | 2018-01-30 | 广东中标数据科技股份有限公司 | A kind of field consanguinity analysis method, system and device based on depth-first traversal |
| CN109325078A (en) * | 2018-09-18 | 2019-02-12 | 拉扎斯网络科技（上海）有限公司 | Data blood margin determination method and device based on structural data |
| CN109446279A (en) * | 2018-10-15 | 2019-03-08 | 顺丰科技有限公司 | Based on neo4j big data genetic connection management method, system, equipment and storage medium |
| CN110245270A (en) * | 2019-05-09 | 2019-09-17 | 重庆天蓬网络有限公司 | Data genetic connection storage method, system, medium and equipment based on graph model |
| CN110232056A (en) * | 2019-05-21 | 2019-09-13 | 苏宁云计算有限公司 | A kind of the blood relationship analytic method and its tool of structured query language |
| CN110555032A (en) * | 2019-09-09 | 2019-12-10 | 北京搜狐新媒体信息技术有限公司 | Data blood relationship analysis method and system based on metadata |
| CN110908997A (en) * | 2019-10-09 | 2020-03-24 | 支付宝(杭州)信息技术有限公司 | Data blood margin construction method and device, server and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 胡晓东;: "分布式顺序表类SQL技术的实现和优化", 现代电子技术, no. 15 * |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112256721B (en) * | 2020-10-21 | 2021-08-17 | 平安科技（深圳）有限公司 | SQL statement parsing method, system, computer device and storage medium |
| CN112256721A (en) * | 2020-10-21 | 2021-01-22 | 平安科技（深圳）有限公司 | SQL statement parsing method, system, computer device and storage medium |
| WO2021179722A1 (en) * | 2020-10-21 | 2021-09-16 | 平安科技（深圳）有限公司 | Sql statement parsing method and system, and computer device and storage medium |
| CN112347123A (en) * | 2020-11-10 | 2021-02-09 | 北京金山云网络技术有限公司 | Data blood margin analysis method and device and server |
| CN112363713A (en) * | 2020-11-30 | 2021-02-12 | 杭州玳数科技有限公司 | Binding type SQL blood margin analysis data flow visualization interaction method |
| CN112363713B (en) * | 2020-11-30 | 2024-06-11 | 杭州玳数科技有限公司 | Visual interaction method for binding type SQL blood-margin analysis data stream |
| CN112464050A (en) * | 2020-12-11 | 2021-03-09 | 中国人寿保险股份有限公司 | Data blood margin sorting method and device based on python and electronic equipment |
| CN112464050B (en) * | 2020-12-11 | 2024-05-28 | 中国人寿保险股份有限公司 | Data blood margin arrangement method and device based on python and electronic equipment |
| CN112434046A (en) * | 2020-12-16 | 2021-03-02 | 杭州天均科技有限公司 | Data blood margin analysis method, device, equipment and storage medium |
| CN112434046B (en) * | 2020-12-16 | 2021-09-17 | 杭州天均科技有限公司 | Data blood margin analysis method, device, equipment and storage medium |
| CN112818015A (en) * | 2021-01-21 | 2021-05-18 | 广州汇通国信科技有限公司 | Data tracking method, system and storage medium based on data blood margin analysis |
| CN112749186B (en) * | 2021-01-22 | 2024-02-09 | 广州虎牙科技有限公司 | Data processing method, device, electronic equipment and computer readable storage medium |
| CN112749186A (en) * | 2021-01-22 | 2021-05-04 | 广州虎牙科技有限公司 | Data processing method and device, electronic equipment and computer readable storage medium |
| CN112765209A (en) * | 2021-02-07 | 2021-05-07 | 浪潮云信息技术股份公司 | SQL statement syntax migration method and system between databases |
| CN112925777A (en) * | 2021-02-25 | 2021-06-08 | 吉林亿联银行股份有限公司 | Method and system for detecting data blood margin of HIVE database |
| WO2022188331A1 (en) * | 2021-03-11 | 2022-09-15 | 北京锐安科技有限公司 | Method and apparatus for presenting data kinship, and electronic device and storage medium |
| CN112860585B (en) * | 2021-03-31 | 2024-01-26 | 中国工商银行股份有限公司 | Test script assertion generation method and device |
| CN112860585A (en) * | 2021-03-31 | 2021-05-28 | 中国工商银行股份有限公司 | Test script assertion generation method and device |
| CN113177057A (en) * | 2021-04-28 | 2021-07-27 | 深圳依时货拉拉科技有限公司 | SQL statement syntax visualization analysis method, system and computer readable storage medium |
| CN113326401B (en) * | 2021-06-16 | 2023-01-20 | 上海哔哩哔哩科技有限公司 | Method and system for generating field blood relationship |
| CN113326401A (en) * | 2021-06-16 | 2021-08-31 | 上海哔哩哔哩科技有限公司 | Method and system for generating field blood margin |
| CN113742368A (en) * | 2021-09-16 | 2021-12-03 | 北京航空航天大学 | Data blood relationship analysis method |
| CN113672628A (en) * | 2021-10-22 | 2021-11-19 | 中航金网(北京)电子商务有限公司 | Data blood margin analysis method, terminal device and medium |
| CN114329082A (en) * | 2021-11-19 | 2022-04-12 | 海纳致远数字科技(上海)有限公司 | Data blood relationship analysis method and system based on hugagraph |
| CN114265945A (en) * | 2021-12-30 | 2022-04-01 | 多点生活(武汉)科技有限公司 | Blood relationship extraction method and device and electronic equipment |
| CN114265945B (en) * | 2021-12-30 | 2024-07-26 | 多点生活(武汉)科技有限公司 | Blood relationship extraction method and device and electronic equipment |
| CN115291889A (en) * | 2022-09-27 | 2022-11-04 | 华控清交信息科技(北京)有限公司 | Data blood relationship establishing method and device and electronic equipment |
| CN118035204A (en) * | 2024-01-25 | 2024-05-14 | 深圳市前海研祥亚太电子装备技术有限公司 | Data blood edge display method, distributed task scheduling system and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111538743B (en) | 2023-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111538743B (en) | SQL-based data blood relationship analysis method and system | |
| US10545998B2 (en) | Syntactic tagging in a domain-specific context | |
| CA2484009C (en) | Managing expressions in a database system | |
| KR101505858B1 (en) | A templet-based online composing system for analyzing reports or views of big data by providing past templets of database tables and reference fields | |
| CN103425740B (en) | A kind of material information search method based on Semantic Clustering of internet of things oriented | |
| CN110674229A (en) | AST-based relational database SQL table relational analysis and display method | |
| CN114218472A (en) | Intelligent search system based on knowledge graph | |
| CN114328471B (en) | Data model based on data virtualization engine and construction method thereof | |
| CN111078729A (en) | Medical data tracing method, device, system, storage medium and electronic equipment | |
| CN104346331A (en) | Retrieval method and system for XML database | |
| CN110717025B (en) | Question answering method and device, electronic equipment and storage medium | |
| KR20130064160A (en) | System of entity-relationship model reformulation of sparql query results on rdf data and the method | |
| CN117149804A (en) | Data processing method, device, electronic equipment and storage medium | |
| CN116680354A (en) | Metadata management method and system for lithium battery production and manufacturing industry | |
| CN116467433A (en) | Knowledge graph visualization method, device, equipment and medium for multi-source data | |
| CN107368500B (en) | Data extraction method and system | |
| CN117076742A (en) | Data blood edge tracking method and device and electronic equipment | |
| CN115456413A (en) | Method, device and equipment for matching personnel with posts and storage medium | |
| CN115080616A (en) | Dictionary data acquisition method and device, storage medium and electronic equipment | |
| CN109657010B (en) | Document processing method, device and storage medium | |
| CN113312517A (en) | Fund knowledge graph obtaining method and device and electronic equipment | |
| CN112988778A (en) | Method and device for processing database query script | |
| US20240220876A1 (en) | Artificial intelligence (ai) based data product provisioning | |
| CN118113882A (en) | Knowledge graph processing method, device, equipment and storage medium | |
| CN116304207A (en) | Data association method and system based on graph database |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |